SBC News Voice of the Sports Betting Industry
AI has really been at the forefront of sports betting conversation in recent years, but it is not a new technology – this observation was made by Andrej Bratko, Sportradar’s Managing Director of AI/BI, during a recent SBC Webinar.
However, although the term itself dates back to the 1950s, making AI ‘almost as old as computing itself’ as a research discipline, he explained – and with the rapid digitisation of betting and gaming over the past decade – the sector is now becoming aware of the tech’s benefits.
A historical trajectory
Providing a breakdown of the history of AI and its development, Bratko explained that ‘deep learning’ – itself a subfield of machine learning – has contributed greatly to the technology’s modern uses.
“This has led to a quantum leap in the capabilities of these technologies, especially when modelling for unstructured data such as text, images, video or audio,” he informed Webinar host Ted Menmuir, SBC Media Content Director.
“Natural language processing has made a giant leap in terms of the capabilities of these models, and with these advances the possible applications of AI have grown exponentially, and this has unlocked a massive market opportunity for AI solutions.”
So how can betting utilise AI? In the eyes of Bratko, and his fellow speakers on the webinar – Sportradar’s Darren Small, Managing Director MTS Managed Betting Services, and Jay Kanabar, Chief Operating Officer of Vaix, a Sportradar subsidiary – the benefits are numerous.
Bratko pointed to odds pricing as an example of a fundamental element of betting operations that ‘really fits well into the traditional definition of AI’.
This is the case ‘especially in in-game, live situations where odds pricing is almost completely automated and driven by models and fit the definition of machine learning’, he added.
“You could say that the betting industry has many years, decades, of experience of application of AI – but that is a specific use case, and generally speaking I do agree that it is not until more recently that we have seen greater adoption of AI to drive different aspects of the industry and operations in a sportsbook.”
Reflecting on Sportradar’s own operations and experience of providing AI to the betting industry, Small noted that operators do not always want to become overly reliant on a third party.
However, in his view, firms such as Sportradar can provide a ‘smarter’ solution for bookmakers, bolstering the latter’s forward facing strategies with a long-term approach.
Sportradar has a lot of diversity in its database, which reduces the danger of there being an ‘echo chamber’, as the company is not hearing the same things ‘over and over again’, he argued.
“We’ve been able to start having conversations with sportsbooks a lot more about the tickets, the real time activation of those tickets and the information surrounding that. That was that historically and still is to a point, a little bit of a blocker for this discussion.
“The more we move on through, the more sportsbooks are becoming more aware of the value that we can bring to the table with the products that we offer.
“Another issue that is sometimes one of the points of discussion is around the integration and the difficulty around that and how much effort is required on their side.”
The power of personalisation
Sharing his own views on AI adoption, Kanabar agreed that the tech can be used to help with customer understanding, as well as personalisation of content.
AI can bring customers the betting content that they want, when they want, strengthening customer engagement in the process.
“In the context of sports betting, AI can be used to bring content in front of the customer based on their past preferences, instead of having to use a search engine,” he said.
“If the customer has a history of preferring football bets, then football is what they will get offered first, for example.”
Small added that AI can be used to identify different segments of punters, as well as specific areas of weakness in a bookmaker’s operation, such as in its trading, for example.
However, he argued that the ultimate benefit for personalisation is that AI can be used to ‘give customers a better journey through what they’re doing’.
This can help guide an operator when deciding whether to allow a customer to increase their deposit limits to allow ‘extra headroom’ for less restrictive play.
He continued: “It also leads into things like lifetime delay models where we have been extremely successful with that work. The stuff that Andre and his team have done there is phenomenal, in my view.
“We look at all the aspects around the customer placing bets and look at all of the points around the live environment, and look to see if there is a high risk applied from a latency abuse perspective from a customer level.”
This in turn means that customers classed as low risk and ‘genuine recreational sports betting customers’ will not be affected by the lifetime delays put in place to protect bookmakers from latency trouble.
The future of AI
The rise in AI adoption by operators is obvious – but to further encourage it, Bratko outlined that providers need to focus on the potential of the technology.
“This requires a combination of the right technology and human resource in place – the productization is a lot more complex, and needs more than just machine learning of models,” he said.
“The industry also needs access to high volumes of high quality data. Data is ‘the new oil’ and the fuel behind any strong AI initiative, and so whoever controls the most data has an advantage.”
Small observed that whilst AI integration is ‘not a flick of the switch process’, Sportradar is continuing to work with bookmaker’s to improve its speed.
Having already been adopted across three main areas of betting operations – trading models, risk management models and marketing models – AI is certainly on the up.
As the speakers observed, this is best demonstrated by the stats – by 2030 AI is projected to generate $15.7trn to the global economy, yield a sports industry valuation of $19.2bn, contribute a 26% rise in GDP to local economies and a 30% increase in CAGR.
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